Статті в журналах з теми "Concept Drift Detection"
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Zhu, Jiaqi, Shaofeng Cai, Fang Deng, Beng Chin Ooi, and Wenqiao Zhang. "METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection." Proceedings of the VLDB Endowment 17, no. 4 (December 2023): 794–807. http://dx.doi.org/10.14778/3636218.3636233.
Повний текст джерелаSakurai, Guilherme Yukio, Jessica Fernandes Lopes, Bruno Bogaz Zarpelão, and Sylvio Barbon Junior. "Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives." Future Internet 15, no. 5 (April 29, 2023): 169. http://dx.doi.org/10.3390/fi15050169.
Повний текст джерелаToor, Affan Ahmed, Muhammad Usman, Farah Younas, Alvis Cheuk M. Fong, Sajid Ali Khan, and Simon Fong. "Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems." Sensors 20, no. 7 (April 9, 2020): 2131. http://dx.doi.org/10.3390/s20072131.
Повний текст джерелаKumar, Sanjeev, Ravendra Singh, Mohammad Zubair Khan, and Abdulfattah Noorwali. "Design of adaptive ensemble classifier for online sentiment analysis and opinion mining." PeerJ Computer Science 7 (August 5, 2021): e660. http://dx.doi.org/10.7717/peerj-cs.660.
Повний текст джерелаDries, Anton, and Ulrich Rückert. "Adaptive concept drift detection." Statistical Analysis and Data Mining: The ASA Data Science Journal 2, no. 5-6 (November 18, 2009): 311–27. http://dx.doi.org/10.1002/sam.10054.
Повний текст джерелаPalli, Abdul Sattar, Jafreezal Jaafar, Heitor Murilo Gomes, Manzoor Ahmed Hashmani, and Abdul Rehman Gilal. "An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams." Applied Sciences 12, no. 22 (November 17, 2022): 11688. http://dx.doi.org/10.3390/app122211688.
Повний текст джерелаHu, Hanqing, and Mehmed Kantardzic. "Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification." Intelligent Decision Technologies 15, no. 4 (January 10, 2022): 609–22. http://dx.doi.org/10.3233/idt-210115.
Повний текст джерелаSun, Yange, Zhihai Wang, Yang Bai, Honghua Dai, and Saeid Nahavandi. "A Classifier Graph Based Recurring Concept Detection and Prediction Approach." Computational Intelligence and Neuroscience 2018 (June 7, 2018): 1–13. http://dx.doi.org/10.1155/2018/4276291.
Повний текст джерелаYOSHIDA, Kenichi. "Brute force concept drift detection." Procedia Computer Science 225 (2023): 1672–81. http://dx.doi.org/10.1016/j.procs.2023.10.156.
Повний текст джерелаWares, Scott, John Isaacs, and Eyad Elyan. "Burst Detection-Based Selective Classifier Resetting." Journal of Information & Knowledge Management 20, no. 02 (April 23, 2021): 2150027. http://dx.doi.org/10.1142/s0219649221500271.
Повний текст джерелаGâlmeanu, Honorius, and Răzvan Andonie. "Concept Drift Adaptation with Incremental–Decremental SVM." Applied Sciences 11, no. 20 (October 15, 2021): 9644. http://dx.doi.org/10.3390/app11209644.
Повний текст джерелаMcKay, Helen, Nathan Griffiths, Phillip Taylor, Theo Damoulas, and Zhou Xu. "Bi-directional online transfer learning: a framework." Annals of Telecommunications 75, no. 9-10 (October 2020): 523–47. http://dx.doi.org/10.1007/s12243-020-00776-1.
Повний текст джерелаLu, Ning, Guangquan Zhang, and Jie Lu. "Concept drift detection via competence models." Artificial Intelligence 209 (April 2014): 11–28. http://dx.doi.org/10.1016/j.artint.2014.01.001.
Повний текст джерелаMulimani, Deepa C., Shashikumar G. Totad, and Prakashgoud R. Patil. "Concept Drift Adaptation in Intrusion Detection Systems Using Ensemble Learning." International Journal of Natural Computing Research 10, no. 4 (October 1, 2021): 1–22. http://dx.doi.org/10.4018/ijncr.2021100101.
Повний текст джерелаKumar, Sanjeev, and Ravendra Singh. "Comparative Analysis of Drift Detection Based Adaptive Ensemble Model with Different Drift Detection Techniques." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 29, 2021): 49–55. http://dx.doi.org/10.51201/jusst/21/06492.
Повний текст джерелаSankara Prasanna Kumar, M., A. P. Siva Kumar, and K. Prasanna. "Data Mining Models of High Dimensional Data Streams, and Contemporary Concept Drift Detection Methods: a Comprehensive Review." International Journal of Engineering & Technology 7, no. 3.6 (July 4, 2018): 148. http://dx.doi.org/10.14419/ijet.v7i3.6.14959.
Повний текст джерелаBarddal, Jean Paul, Heitor Murilo Gomes, and Fabrício Enembreck. "Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory." International Journal of Natural Computing Research 5, no. 1 (January 2015): 26–41. http://dx.doi.org/10.4018/ijncr.2015010102.
Повний текст джерелаAlthabiti, Mashail Shaeel, and Manal Abdullah. "CDDM: Concept Drift Detection Model for Data Stream." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 10 (June 30, 2020): 90. http://dx.doi.org/10.3991/ijim.v14i10.14803.
Повний текст джерелаSheluhin, Oleg I., Vyacheslav V. Barkov, and Airapet G. Simonyan. "Concept drift detection in mobile applications classification using autoencoders." H&ES Research 15, no. 3 (2023): 20–29. http://dx.doi.org/10.36724/2409-5419-2023-15-3-20-29.
Повний текст джерелаChu, Renjie, Peiyuan Jin, Hanli Qiao, and Quanxi Feng. "Intrusion detection in the IoT data streams using concept drift localization." AIMS Mathematics 9, no. 1 (2023): 1535–61. http://dx.doi.org/10.3934/math.2024076.
Повний текст джерелаLEE, Jeonghoon, and Yoon-Joon LEE. "Concept Drift Detection for Evolving Stream Data." IEICE Transactions on Information and Systems E94-D, no. 11 (2011): 2288–92. http://dx.doi.org/10.1587/transinf.e94.d.2288.
Повний текст джерелаBeshah, Yonas Kibret, Surafel Lemma Abebe, and Henock Mulugeta Melaku. "Drift Adaptive Online DDoS Attack Detection Framework for IoT System." Electronics 13, no. 6 (March 7, 2024): 1004. http://dx.doi.org/10.3390/electronics13061004.
Повний текст джерелаDesale, Ketan Sanjay, and Swati Shinde. "Real-Time Concept Drift Detection and Its Application to ECG Data." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 10 (October 19, 2021): 160. http://dx.doi.org/10.3991/ijoe.v17i10.25473.
Повний текст джерелаMehmood, Tajwar, Seemab Latif, Nor Shahida Mohd Jamail, Asad Malik, and Rabia Latif. "LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing." PeerJ Computer Science 10 (January 31, 2024): e1827. http://dx.doi.org/10.7717/peerj-cs.1827.
Повний текст джерелаSubha, S., and J. G. R. Sathiaseelan. "Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique in IoT Healthcare Data." Indian Journal Of Science And Technology 17, no. 5 (January 31, 2024): 386–96. http://dx.doi.org/10.17485/ijst/v17i5.1645.
Повний текст джерелаAbdualrhman, Mohammed Ahmed Ali, and M. C. Padma. "Deterministic Concept Drift Detection in Ensemble Classifier Based Data Stream Classification Process." International Journal of Grid and High Performance Computing 11, no. 1 (January 2019): 29–48. http://dx.doi.org/10.4018/ijghpc.2019010103.
Повний текст джерелаAdebayo, Oluwadare Samuel, Thompson Aderonke Favour-Bethy, Owolafe Otasowie, and Orogun Adebola Okunola. "Comparative Review of Credit Card Fraud Detection using Machine Learning and Concept Drift Techniques." International Journal of Computer Science and Mobile Computing 12, no. 7 (July 30, 2023): 24–48. http://dx.doi.org/10.47760/ijcsmc.2023.v12i07.004.
Повний текст джерелаManikandaraja, Abishek, Peter Aaby, and Nikolaos Pitropakis. "Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection." Computers 12, no. 10 (September 28, 2023): 195. http://dx.doi.org/10.3390/computers12100195.
Повний текст джерелаNamitha K. and Santhosh Kumar G. "Concept Drift Detection in Data Stream Clustering and its Application on Weather Data." International Journal of Agricultural and Environmental Information Systems 11, no. 1 (January 2020): 67–85. http://dx.doi.org/10.4018/ijaeis.2020010104.
Повний текст джерелаLi, Xiangjun, Yong Zhou, Ziyan Jin, Peng Yu, and Shun Zhou. "A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance." Journal of Electrical and Computer Engineering 2020 (March 19, 2020): 1–8. http://dx.doi.org/10.1155/2020/4027423.
Повний текст джерелаOmori, Nicolas Jashchenko, Gabriel Marques Tavares, Paolo Ceravolo, and Sylvio Barbon Jr. "Comparing Concept Drift Detection with Process Mining Software." iSys - Brazilian Journal of Information Systems 13, no. 4 (July 31, 2020): 101–25. http://dx.doi.org/10.5753/isys.2020.832.
Повний текст джерелаDu, L., Q. Song, L. Zhu, and X. Zhu. "A Selective Detector Ensemble for Concept Drift Detection." Computer Journal 58, no. 3 (June 20, 2014): 457–71. http://dx.doi.org/10.1093/comjnl/bxu050.
Повний текст джерелаZambon, Daniele, Cesare Alippi, and Lorenzo Livi. "Concept Drift and Anomaly Detection in Graph Streams." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (November 2018): 5592–605. http://dx.doi.org/10.1109/tnnls.2018.2804443.
Повний текст джерелаCabral, Danilo Rafael de Lima, and Roberto Souto Maior de Barros. "Concept drift detection based on Fisher’s Exact test." Information Sciences 442-443 (May 2018): 220–34. http://dx.doi.org/10.1016/j.ins.2018.02.054.
Повний текст джерелаAdams, Jan Niklas, Cameron Pitsch, Tobias Brockhoff, and Wil M. P. van der Aalst. "An Experimental Evaluation of Process Concept Drift Detection." Proceedings of the VLDB Endowment 16, no. 8 (April 2023): 1856–69. http://dx.doi.org/10.14778/3594512.3594517.
Повний текст джерелаSun, Yingying, Jusheng Mi, and Chenxia Jin. "Entropy-based concept drift detection in information systems." Knowledge-Based Systems 290 (April 2024): 111596. http://dx.doi.org/10.1016/j.knosys.2024.111596.
Повний текст джерелаGandhi, Jay, and Vaibhav Gandhi. "Novel Class Detection with Concept Drift in Data Stream - AhtNODE." International Journal of Distributed Systems and Technologies 11, no. 1 (January 2020): 15–26. http://dx.doi.org/10.4018/ijdst.2020010102.
Повний текст джерелаMahdi, Osama A., Eric Pardede, Nawfal Ali, and Jinli Cao. "Fast Reaction to Sudden Concept Drift in the Absence of Class Labels." Applied Sciences 10, no. 2 (January 14, 2020): 606. http://dx.doi.org/10.3390/app10020606.
Повний текст джерелаPalli, Abdul Sattar, Jafreezal Jaafar, Abdul Rehman Gilal, Aeshah Alsughayyir, Heitor Murilo Gomes, Abdullah Alshanqiti, and Mazni Omar. "Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review." Journal of Information and Communication Technology 23, no. 1 (January 30, 2024): 105–39. http://dx.doi.org/10.32890/jict2024.23.1.5.
Повний текст джерелаSato, Denise Maria Vecino, Sheila Cristiana De Freitas, Jean Paul Barddal, and Edson Emilio Scalabrin. "A Survey on Concept Drift in Process Mining." ACM Computing Surveys 54, no. 9 (December 31, 2022): 1–38. http://dx.doi.org/10.1145/3472752.
Повний текст джерелаVyawhare, Chaitanya R., Reshma Y. Totare, Prashant S. Sonawane, and Purva B. Deshmukh. "Machine Learning System for Malicious Website Detection using Concept Drift Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 47–55. http://dx.doi.org/10.22214/ijraset.2022.42048.
Повний текст джерелаElkhawaga, Ghada, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad, and Manfred Reichert. "CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining." Algorithms 13, no. 7 (July 3, 2020): 161. http://dx.doi.org/10.3390/a13070161.
Повний текст джерелаHenke, Marcia, Eulanda Santos, Eduardo Souto, and Altair O. Santin. "Spam Detection Based on Feature Evolution to Deal with Concept Drift." JUCS - Journal of Universal Computer Science 27, no. 4 (April 28, 2021): 364–86. http://dx.doi.org/10.3897/jucs.66284.
Повний текст джерелаYang, Rui, Shuliang Xu, and Lin Feng. "An Ensemble Extreme Learning Machine for Data Stream Classification." Algorithms 11, no. 7 (July 17, 2018): 107. http://dx.doi.org/10.3390/a11070107.
Повний текст джерелаChen, Xue, Yang Song, Wei Xiong, Yutao Lu, and Xingen Wang. "Research on Web Robot Detection Technology for Concept Drift." Journal of Physics: Conference Series 2010, no. 1 (September 1, 2021): 012161. http://dx.doi.org/10.1088/1742-6596/2010/1/012161.
Повний текст джерелаMiyata, Yasushi, and Hiroshi Ishikawa. "Concept Drift Detection on Stream Data for Revising DBSCAN." IEEJ Transactions on Electronics, Information and Systems 140, no. 8 (August 1, 2020): 949–55. http://dx.doi.org/10.1541/ieejeiss.140.949.
Повний текст джерелаCejnek, Matous, and Ivo Bukovsky. "Concept drift robust adaptive novelty detection for data streams." Neurocomputing 309 (October 2018): 46–53. http://dx.doi.org/10.1016/j.neucom.2018.04.069.
Повний текст джерелаEscovedo, Tatiana, Adriano Koshiyama, Andre Abs da Cruz, and Marley Vellasco. "DetectA: abrupt concept drift detection in non-stationary environments." Applied Soft Computing 62 (January 2018): 119–33. http://dx.doi.org/10.1016/j.asoc.2017.10.031.
Повний текст джерелаZenisek, Jan, Florian Holzinger, and Michael Affenzeller. "Machine learning based concept drift detection for predictive maintenance." Computers & Industrial Engineering 137 (November 2019): 106031. http://dx.doi.org/10.1016/j.cie.2019.106031.
Повний текст джерелаYu, Shujian, Zubin Abraham, Heng Wang, Mohak Shah, Yantao Wei, and José C. Príncipe. "Concept drift detection and adaptation with hierarchical hypothesis testing." Journal of the Franklin Institute 356, no. 5 (March 2019): 3187–215. http://dx.doi.org/10.1016/j.jfranklin.2019.01.043.
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